MétaCan
Menu
Back to cohort
Record W3104001696 · doi:10.1371/journal.pone.0241144

Moral “foundations” as the product of motivated social cognition: Empathy and other psychological underpinnings of ideological divergence in “individualizing” and “binding” concerns

2020· article· en· W3104001696 on OpenAlexfundno aff
Michael Strupp-Levitsky, Sharareh Noorbaloochi, Andrew Shipley, John T. Jost

Bibliographic record

VenuePLoS ONE · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsnot available
FundersYork University
KeywordsIdeologyEmpathyExistentialismHarmEpistemologySocial cognitive theory of moralityMoral psychologyPsychologyMoral disengagementSocial psychologyPoliticsCognitionSociologyPhilosophyPolitical scienceLaw

Abstract

fetched live from OpenAlex

According to moral foundations theory, there are five distinct sources of moral intuition on which political liberals and conservatives differ. The present research program seeks to contextualize this taxonomy within the broader research literature on political ideology as motivated social cognition, including the observation that conservative judgments often serve system-justifying functions. In two studies, a combination of regression and path modeling techniques were used to explore the motivational underpinnings of ideological differences in moral intuitions. Consistent with our integrative model, the "binding" foundations (in-group loyalty, respect for authority, and purity) were associated with epistemic and existential needs to reduce uncertainty and threat and system justification tendencies, whereas the so-called "individualizing" foundations (fairness and avoidance of harm) were generally unrelated to epistemic and existential motives and were instead linked to empathic motivation. Taken as a whole, these results are consistent with the position taken by Hatemi, Crabtree, and Smith that moral "foundations" are themselves the product of motivated social cognition.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.297
GPT teacher head0.387
Teacher spread0.091 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations54
Published2020
Admission routes1
Has abstractyes

Explore more

Same venuePLoS ONESame topicSocial and Intergroup PsychologyFrench-language works237,207